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Open accessJournal ArticleDOI: 10.1080/19490976.2021.1888673

Links between gut microbiome composition and fatty liver disease in a large population sample.

02 Mar 2021-Gut microbes (Taylor & Francis)-Vol. 13, Iss: 1, pp 1-22
Abstract: Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 y, but this association remains mostly unstudied in the gene...

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Topics: Fatty liver (58%), Liver disease (57%)

12 results found

Open accessPosted ContentDOI: 10.1101/2020.09.12.20193045
Youwen Qin1, Aki S. Havulinna2, Liu Yang1, Pekka Jousilahti2  +17 moreInstitutions (8)
13 Sep 2020-medRxiv
Abstract: Co-evolution between humans and the microbial communities colonizing them has resulted in an intimate assembly of thousands of microbial species mutualistically living on and in their body and impacting multiple aspects of host physiology and health. Several studies examining whether human genetic variation can affect gut microbiota suggest a complex combination of environmental and host factors. Here, we leverage a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial shotgun metagenomes, dietary information and health records up to 16 years post-sampling, to characterize human genetic variations associated with microbial abundances, and predict possible causal links with various diseases using Mendelian randomization (MR). Genome-wide association study (GWAS) identified 583 independent SNP-taxon associations at genome-wide significance (p

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Topics: Population (55%), Mendelian randomization (53%), Gut flora (51%)

7 Citations

Open accessJournal ArticleDOI: 10.1097/CM9.0000000000000734
Jing Wang1, Zhong-Hua Shi2, Jing Yang1, Yuan Wei1  +2 moreInstitutions (2)
Abstract: BACKGROUND Preeclampsia (PE) is a serious complication that affects maternal and perinatal outcomes. However, the mechanisms have not been fully explained. This study was designed to analyze longitudinal gut microbiota alterations in pregnant women with and without PE in the second (T2) and third trimesters (T3). METHODS In this nested case-control study, which was conducted at Nanjing Maternity and Child Health Care Hospital, fecal samples from 25 PE patients (25 fecal samples obtained in T2 and 15 fecal samples obtained in T3) and 25 matched healthy controls (25 fecal samples obtained in T2 and 22 fecal samples obtained in T3) were collected, and the microbiota were analyzed using 16S rRNA gene sequencing. The diversity and composition of the microbiota of PE cases and controls were compared. RESULTS No significant differences in diversity were found between the PE and control groups (P > 0.05). In the control group, from T2 to T3, the relative abundances of Proteobacteria (median [Q1, Q3]: 2.25% [1.24%, 3.30%] vs. 0.64% [0.20%, 1.20%], Z = -3.880, P < 0.05), and Tenericutes (median [Q1, Q3]: 0.12% [0.03%, 3.10%] vs. 0.03% [0.02%, 0.17%], Z = -2.369, P < 0.05) decreased significantly. In the PE group, the relative abundance of Bacteroidetes in T2 was lower than in T3 (median [Q1, Q3]: 18.16% [12.99%, 30.46%] vs. 31.09% [19.89%, 46.06%], Z = -2.417, P < 0.05). In T2, the relative abundances of mircrobiota showed no significant differences between the PE group and the control group. However, in T3, the relative abundance of Firmicutes was significantly lower in the PE group than in the control group (mean ± standard deviation: 60.62% ± 15.17% vs. 75.57% ± 11.53%, t = -3.405, P < 0.05). The relative abundances of Bacteroidetes, Proteobacteria, and Enterobacteriaceae were significantly higher in the PE group than in the control group (median [Q1, Q3]: 31.09% [19.89%, 46.06%] vs. 18.24% [12.90%, 32.04%], Z = -2.537, P < 0.05; 1.52% [1.05%, 2.61%] vs. 0.64% [0.20%, 1.20%], Z = -3.310, P < 0.05; 0.75% [0.20%, 1.00%] vs. 0.01% [0.004%, 0.023%], Z = -4.152, P < 0.05). Linear discriminant analysis combined effect size measurements analysis showed that the relative abundances of the phylum Bacteroidetes, class Bacteroidia and order Bacteroidales were increased in the PE group, while those of the phylum Firmicutes, the class Clostridia, the order Clostridiales, and the genus unidentified Lachnospiraceae were decreased in the PE group; and these differences were identified as taxonomic biomarkers of PE in T3. CONCLUSION From T2 to T3, there was an obvious alteration in the gut microbiota. The gut microbiota of PE patients in T3 was significantly different from that of the control group.

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6 Citations

Open accessJournal ArticleDOI: 10.1111/ECI.13622
Abstract: According to the 'multiple-hit' hypothesis, several factors can act simultaneously in nonalcoholic fatty liver disease (NAFLD) progression. Increased nitro-oxidative (nitroso-oxidative) stress may be considered one of the main contributors involved in the development and risk of NAFLD progression to nonalcoholic steatohepatitis (NASH) characterized by inflammation and fibrosis. Moreover, it has been repeatedly postulated that mitochondrial abnormalities are closely related to the development and progression of liver steatosis and NAFLD pathogenesis. However, it is difficult to determine with certainty whether mitochondrial dysfunction or oxidative stress are primary events or a simple consequence of NAFLD development. On the one hand, increasing lipid accumulation in hepatocytes could cause a wide range of effects from mild to severe mitochondrial damage with a negative impact on cell fate. This can start the cascade of events, including an increase of cellular reactive nitrogen species (RNS) and reactive oxygen species (ROS) production that promotes disease progression from simple steatosis to more severe NAFLD stages. On the other hand, progressing mitochondrial bioenergetic catastrophe and oxidative stress manifestation could be considered accompanying events in the vast spectrum of abnormalities observed during the transition from NAFL to NASH and cirrhosis. This review updates our current understanding of NAFLD pathogenesis and clarifies whether mitochondrial dysfunction and ROS/RNS are culprits or bystanders of NAFLD progression.

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2 Citations

Open accessJournal ArticleDOI: 10.3390/METABO11060353
31 May 2021-Metabolites
Abstract: The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing, as are other manifestations of metabolic syndrome such as obesity and type 2 diabetes. NAFLD is currently the number one cause of chronic liver disease worldwide. The pathophysiology of NAFLD and disease progression is poorly understood. A potential contributing role for gut microbiome and metabolites in NAFLD is proposed. Currently, bariatric surgery is an effective therapy to prevent the progression of NAFLD and other manifestations of metabolic syndrome such as obesity and type 2 diabetes. This review provides an overview of gut microbiome composition and related metabolites in individuals with NAFLD and after bariatric surgery. Causality remains to be proven. Furthermore, the clinical effects of bariatric surgery on NAFLD are illustrated. Whether the gut microbiome and metabolites contribute to the metabolic improvement and improvement of NAFLD seen after bariatric surgery has not yet been proven. Future microbiome and metabolome research is necessary for elucidating the pathophysiology and underlying metabolic pathways and phenotypes and providing better methods for diagnostics, prognostics and surveillance to optimize clinical care.

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Topics: Microbiome (53%)

2 Citations

Open accessJournal ArticleDOI: 10.1111/1462-2920.15462
Abstract: Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.

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Topics: Spatial ecology (58%), Ecology (disciplines) (54%), Applied ecology (51%)

1 Citations


101 results found

Open accessJournal ArticleDOI: 10.1038/NMETH.1923
01 Apr 2012-Nature Methods
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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27,973 Citations

Open accessProceedings ArticleDOI: 10.1145/2939672.2939785
Tianqi Chen1, Carlos Guestrin1Institutions (1)
13 Aug 2016-
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

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Topics: Incremental decision tree (64%), Gradient boosting (61%), ID3 algorithm (60%) ... read more

10,428 Citations

Open accessJournal ArticleDOI: 10.1371/JOURNAL.PONE.0061217
Paul J. McMurdie1, Susan Holmes1Institutions (1)
22 Apr 2013-PLOS ONE
Abstract: Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

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Topics: Bioconductor (53%)

7,065 Citations

Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTU153
Torsten Seemann1Institutions (1)
15 Jul 2014-Bioinformatics
Abstract: UNLABELLED: The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. AVAILABILITY AND IMPLEMENTATION: Prokka is implemented in Perl and is freely available under an open source GPLv2 license from

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Topics: Genome project (58%), Sequence assembly (51%), Perl (51%) ... read more

6,934 Citations

Open accessBook ChapterDOI: 10.1007/978-3-319-58347-1_10
Yaroslav Ganin1, Evgeniya Ustinova1, Hana Ajakan2, Pascal Germain2  +4 moreInstitutions (3)
Abstract: We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.

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Topics: Semi-supervised learning (60%), Domain (software engineering) (58%), Feature learning (56%) ... read more

4,760 Citations

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